What would it take to challenge Nvidia? SemiAnalysis Founder and CEO Dylan Patel joins Sarah Guo to answer this and other topical questions around the current state of AI infrastructure. Together, they explore why Dylan loves Android products, predictions around OpenAI’s open source model, and what the landscape of neoclouds looks like. They also discuss Dylan’s thoughts on bottlenecks for expanding AI infrastructure and exporting American AI technologies. Plus, we find out what question Dylan would ask Mark Zuckerberg. 

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Chapters:

00:00 – Dylan Patel Introduction

00:31 – Dylan’s Love for Android Products

02:10 – Predictions About OpenAI’s Open Source Model

06:50 – Implications of an American Open Source Model for the Application Ecosystem

10:48 – Evolution of Neoclouds

17:26 – What It Would Take to Challenge Nvidia

27:43 – What Would an Nvidia Challenger Look Like?

28:18 – Understanding Operational and Power Constraints for Data Centers

34:48 – Dylan’s View on the American Stack

43:01 – What Dylan Would Ask Mark Zuckerberg

44:22 – Poker and AI Entrepreneurship

46:51 – Conclusion

Cloudflare has spent nearly fifteen years making the Internet faster, more reliable, and more secure. So now that AI systems are changing the way we interact with the Internet, Cloudflare wants to help level the playing field for content creators. Sarah Guo and Elad Gil sit down with Matthew Prince, co-founder and CEO of Cloudflare to discuss the evolution of the internet from search to AI, including Cloudflare’s role in facilitating that shift. Matthew talks about how AI assistants are changing the shape of the Internet, the problems Google created by making traffic the arbiter of content value, and how he sees Cloudflare’s part in facilitating the new content marketplace for the mutual benefit of creators and AI companies. Plus, a look towards how agentic infrastructure may unfold in the near future.

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Chapters:

00:00 – Matthew Prince Introduction

00:37 – Cloudflare’s Role in Securing the Internet

02:08 – The Road to Cloudflare’s Dominance

03:20 – The Internet’s Shift from Search to AI

06:34 – Role of Agents and Content on the New Web

09:44 – Reshaping the Content Market Online

13:05 – De-emphasizing Traffic as a Proxy for Value

18:04 – Will We Run Out of Quality Human-Generated Content?

20:01 – Scaling the Value of Content in the AI Age

22:32 – Cloudflare’s Approach to Inference

24:55 – How Cloudflare Responds to Market Demand

26:04 – Open vs. Closed Models

27:21 – Path to the New Marketplace for Content

30:58 – Advice for Content Creators

32:47 – Exploring the Timeline for Running Models Locally

40:07 – The Future of Agentic Infrastructure

44:52 – Conclusion

Sriram Krishnan was never interested in policy. But after seeing a gap in AI knowledge at senior levels of government, he decided to lend his expertise to the tech-friendly Trump administration. Senior White House Policy Advisor on AI Sriram Krishnan joins Elad Gil and Sarah Guo to talk about America’s AI Action Plan, a recent executive order that outlines how America can win the AI race and maintain its AI supremacy. Sriram discusses why winning the AI race is important and what that looks like, as well as the core goals of the Action Plan that he helped to author. Together, they explore how AI is the latest iteration of American cultural exportation and soft power, the bottlenecks in upgrading America’s energy infrastructure, and the importance of America owning the “full stack” from GPUs and models to agents and software.

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Chapters:

00:00 – Sriram Krishnan Introduction

01:00 – Sriram’s Role in Government

03:43 – Impetus for the America AI Action Plan

06:14 – What Winning the AI Race Looks Like

10:36 – Algorithms and Cultural Bias

12:26 – Main Tenets of the America AI Action Plan

19:13 – Infrastructure and Energy Needs for AI

22:56 – Manufacturing, Supply Chains, and AI

24:52 – Ensuring American Dominance in Robotics

26:30 – Translating Policy to Industry and the Economy

29:30 – Should the US Be a Technocracy?

32:33 – Understanding the Argument Against Open Source Models

36:07 – Conclusion

In the generative AI revolution, quality data is a valuable commodity. But not all data is created equally. Sarah Guo and Elad Gil sit down with SurgeAI founder and CEO Edwin Chen to discuss the meaning and importance of quality human data. Edwin talks about why he bootstrapped Surge instead of raising venture funds, the importance of scalable oversight in producing quality data, and the work Surge is doing to standardize human evals. Plus, we get Edwin’s take on what Meta’s investment into Scale AI means for Surge, as well as whether or not he thinks an underdog can catch up with OpenAI, Anthropic, and other dominant industry players.

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Chapters:

00:00 – Edwin Chen Introduction

00:41 – Overview of SurgeAI

02:28 – Why SurgeAI Bootstrapped Instead of Raising Funds

07:59 – Explaining SurgeAI’s Product

09:39 – Differentiating SurgeAI from Competitors 

11:27 – Measuring the Quality of SurgeAI’s Output

12:25 – Role of Scalable Oversight at SurgeAI

14:02 – Challenges of Building Rich RL Environments

16:39 – Predicting Future Needs for Training AI Models

17:29 – Role of Humans in Data Generation

21:27 – Importance of Human Evaluation for Quality Data

22:51 – SurgeAI’s Work Toward Standardization of Human Evals

23:37 – What the Meta/ScaleAI Deal Means for SurgeAI

24:35 – Edwin’s Underdog Pick to Catch Up to Big AI Companies

24:50 – The Future Frontier Model Landscape

26:25 – Future Directions for SurgeAI

29:29 – What Does High Quality Data Mean?

32:26 – Conclusion

Superintelligence, at least in an academic sense, has already been achieved. But Misha Laskin thinks that the next step towards artificial superintelligence, or ASI, should look both more user and problem-focused. ReflectionAI co-founder and CEO Misha Laskin joins Sarah Guo to introduce Asimov, their new code comprehension agent built on reinforcement learning (RL). Misha talks about creating tools and designing AI agents based on customer needs, and how that influences eval development and the scope of the agent’s memory. The two also discuss the challenges in solving scaling for RL, the future of ASI, and the implications for Google’s “non-acquisition” of Windsurf. 

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Chapters:

00:00 – Misha Laskin Introduction

00:44 – Superintelligence vs. Super Intelligent Autonomous Systems

03:26 – Misha’s Journey from Physics to AI

07:48 – Asimov Product Release

11:52 – What Differentiates Asimov from Other Agents

16:15 – Asimov’s Eval Philosophy

21:52 – The Types of Queries Where Asimov Shines

24:35 – Designing a Team-Wide Memory for Asimov

28:38 – Leveraging Pre-Trained Models

32:47 – The Challenges of Solving Scaling in RL

37:21 – Training Agents in Copycat Software Environments

38:25 – When Will We See ASI? 

44:27 – Thoughts on Windsurf’s Non-Acquisition

48:10 – Exploring Non-RL Datasets

55:12 – Tackling Problems Beyond Engineering and Coding

57:54 – Where We’re At in Deploying ASI in Different Fields

01:02:30 – Conclusion

As a three-time founder, Parker Conrad has one piece of advice for aspiring entrepreneurs—don’t do it. The Rippling co-founder and CEO joins Sarah Guo to talk about what he learned from the crash at Zenefits, why most advice to founders is wrong, and how building a real platform—not a point solution—is the only way to win in SaaS. The two get into founder psychology, the myth of learning from failure, and what true ownership looks like inside a company. He also shares why AI won’t shrink teams anytime soon, what people misunderstand about vertical software, and why ambition trumps efficiency with long-lasting companies.

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Chapters:

00:00 Introduction to Parker Conrad

00:33 Lessons from Zenefits to Rippling

01:54 The Psychology of Founding a Company

07:56 Rippling's Ambitious Vision

10:41 Building a Platform Company

15:05 Challenges and Strategies in Scaling

30:36 AI's Impact on Software Development

42:06 Public vs. Private: Rippling's Future

44:19 Conclusion 

AI has already fueled breakthroughs in biotechnology—but now, further advances in AI are poised to fuel pharmaceutical discoveries as well. Sarah Guo sits down with Joshua Meier and Jack Dent, co-founders of Chai Discovery, whose newly launched Chai-2 designs bespoke antibodies that bind to their targets at a jaw-dropping 20% rate. Jack and Joshua talk about the implications for Chai-2’s success rate at discovering antibodies for the pharmaceutical industry, how structure prediction is pivotal in making the model work, and future potential for using the model to optimize other molecular properties. Plus, they talk about what they believe bioscientists should be learning to best utilize Chai-2’s technology. 

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Chapters:

00:00 – Joshua Meier and Jack Dent Introduction

01:09 – Genesis of Chai Discovery

06:12 – Chai-2 Model

10:13 – Criteria for Specifying Targets for Chai-2

13:12 – How the Chai-2 Model Works

16:12 – Emergent Vocabulary from Chai-2

18:15 – Hopes for Chai-2’s Impact

20:33 – Reception of the Chai-2 Model

22:16 – Future of Wet Lab Screening and Biotech

27:08 – Optimizing Other Molecule Properties

31:37 – Where Chai Invests From Here

36:20 – What Bioscientists Should Learn for Chai-2

40:23 – How Jack and Josh Oriented to the Biotech Space

43:38 – Platform Investment and Chai-2

46:53 – Scaling Chai Discovery

48:21 – Hiring at Chai Discovery

49:09 – Conclusion

Much of the scientific process involves searching. But rather than continue to rely on the luck of discovery, Google DeepMind has engineered a more efficient AI agent that mines complex spaces to facilitate scientific breakthroughs. Sarah Guo speaks with Pushmeet Kohli, VP of Science and Strategic Initiatives, and research scientist Matej Balog at Google DeepMind about AlphaEvolve, an autonomous coding agent they developed that finds new algorithms through evolutionary search. Pushmeet and Matej talk about how AlphaEvolve tackles the problem of matrix multiplication efficiency, scaling and iteration in problem solving, and whether or not this means we are at self-improving AI. Together, they also explore the implications AlphaEvolve has to other sciences beyond mathematics and computer science.

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Chapters:

00:00 Pushmeet Kohli and Matej Balog Introduction

0:48 Origin of AlphaEvolve

02:31 AlphaEvolve’s Progression from AlphaGo and AlphaTensor

08:02 The Open Problem of Matrix Multiplication Efficiency

11:18 How AlphaEvolve Evolves Code

14:43 Scaling and Predicting Iterations

16:52 Implications for Coding Agents

19:42 Overcoming Limits of Automated Evaluators

25:21 Are We At Self-Improving AI?

28:10 Effects on Scientific Discovery and Mathematics

31:50 Role of Human Scientists with AlphaEvolve

38:30 Making AlphaEvolve Broadly Accessible

40:18 Applying AlphaEvolve Within Google

41:39 Conclusion

When will fully autonomous vehicles see widespread adoption? According to Applied Intuition, that future is closer than you may think. Applied Intuition’s CEO, Qasar Younis, and CTO, Peter Ludwig, talk with Elad Gil about how now is the best time to both work on self-driving vehicle technology and monetize it. Qasar and Peter discuss the advantages of developing their own OS in-house for their autonomous applications, self-driving technology’s potential to drive re-shoring of vehicle manufacturing to the United States, and how best to gauge the bar for safety in autonomous systems. Plus, they explore how self-driving technology may reshape the designs of not only vehicles, but cities themselves.

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Chapters:

00:00 Qasar Younis and Peter Ludwig Introduction

01:28 A Primer on Applied Intuition

11:08 Applied Intuition’s Customers

12:04 Impact of Chinese Vehicles Manufacturers

15:44 EV Policies in the European Market

20:49 Can Robotics and Automation Re-Shore Vehicle Manufacturing?

21:53 Training Models for Autonomous Vehicles

26:41 Gauging the Bar for Autonomous Vehicles Safety

32:03 Timeline for Large-Scale Autonomous Vehicle Adoption

36:28 Rethinking Urban Design for Autonomous Vehicles

38:47 How Applied Intuition Uses AI for Tooling and OS

42:09 Designing for User Experience

43:31 Applied Intuition’s Hiring Strategy

45:01 Conclusion

What happens when you give AI researchers unlimited compute and tell them to compete for the highest usage rates? Ben Mann, Co-Founder, from Anthropic sits down with Sarah Guo and Elad Gil to explain how Claude 4 went from "reward hacking" to efficiently completing tasks and how they're racing to solve AI safety before deploying computer-controlling agents. Ben talks about economic Turing tests, the future of general versus specialized AI models, Reinforcement Learning From AI Feedback (RLAIF), and Anthropic’s Model Context Protocol (MCP). Plus, Ben shares his thoughts on if we will have Superintelligence by 2028. 

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Links: 

ai-2027.com/ 

Chapters:

00:00 Ben Mann Introduction

00:33 Releasing Claude 4

02:05 Claude 4 Highlights and Improvements

03:42 Advanced Use Cases and Capabilities

06:42 Specialization and Future of AI Models

09:35 Anthropic's Approach to Model Development

18:08 Human Feedback and AI Self-Improvement

19:15 Principles and Correctness in Model Training

20:58 Challenges in Measuring Correctness

21:42 Human Feedback and Preference Models

23:38 Empiricism and Real-World Applications

27:02 AI Safety and Ethical Considerations

28:13 AI Alignment and High-Risk Research

30:01 Responsible Scaling and Safety Policies

35:08 Future of AI and Emerging Behaviors

38:35 Model Context Protocol (MCP) and Industry Standards

41:00 Conclusion